
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Copyright (c) Open-MMLab. All rights reserved.    
_base_ = [
    '../_base_/models/retinanet_r50_fpn.py',
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

# data
data = dict(samples_per_gpu=8)

# optimizer
model = dict(
    backbone=dict(
        depth=18,
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
    neck=dict(in_channels=[64, 128, 256, 512]))

# Note: If the learning rate is set to 0.0025, the mAP will be 32.4.
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)

# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (1 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)
